IELTS Reading: The Role of AI in Improving Productivity – Đề thi mẫu có đáp án chi tiết

Trí tuệ nhân tạo (AI) đang cách mạng hóa cách chúng ta làm việc và nâng cao năng suất trong mọi lĩnh vực. Chủ đề “The Role Of AI In Improving Productivity” xuất hiện ngày càng thường xuyên trong IELTS Reading, đặc biệt trong các đề thi từ năm 2020 trở lại đây, phản ánh tầm quan trọng của công nghệ trong cuộc sống hiện đại.

Bài viết này cung cấp một đề thi IELTS Reading hoàn chỉnh với 3 passages theo đúng format thi thật, từ độ khó Easy đến Hard. Bạn sẽ được luyện tập với đa dạng dạng câu hỏi phổ biến trong IELTS như Multiple Choice, True/False/Not Given, Matching Headings, và Summary Completion. Mỗi passage đều kèm theo đáp án chi tiết với giải thích cụ thể, giúp bạn hiểu rõ cách xác định thông tin và paraphrase trong bài đọc. Ngoài ra, bạn sẽ học được hơn 40 từ vựng học thuật quan trọng liên quan đến công nghệ và năng suất lao động, kèm theo phiên âm, nghĩa và cách sử dụng thực tế.

Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với áp lực thời gian và độ khó tăng dần trong bài thi thật. Hãy chuẩn bị đồng hồ, giấy nháp và bắt đầu làm bài ngay!

Hướng dẫn làm bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng được tính 1 điểm, và tổng điểm sẽ được quy đổi thành band score từ 0-9.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (độ khó dễ, band 5.0-6.5)
• Passage 2: 18-20 phút (độ khó trung bình, band 6.0-7.5)
• Passage 3: 23-25 phút (độ khó cao, band 7.0-9.0)

Lưu ý: Không có thời gian chuyển đáp án riêng, bạn phải ghi đáp án vào answer sheet trong 60 phút.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến:

1. Multiple Choice – Chọn đáp án đúng từ A/B/C/D
2. True/False/Not Given – Xác định thông tin đúng/sai/không đề cập
3. Yes/No/Not Given – Xác định quan điểm tác giả
4. Matching Headings – Nối tiêu đề với đoạn văn
5. Summary Completion – Điền từ vào tóm tắt
6. Matching Features – Nối thông tin với đối tượng
7. Short-answer Questions – Trả lời ngắn theo yêu cầu

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IELTS Reading Practice Test

PASSAGE 1 – Artificial Intelligence in the Modern Workplace

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The introduction of artificial intelligence into the workplace has transformed how businesses operate and how employees perform their daily tasks. Over the past decade, AI technologies have moved from being experimental tools used only by tech giants to becoming mainstream solutions adopted by companies of all sizes. This shift has been driven by the accessibility of AI platforms and the clear benefits they offer in terms of productivity enhancement.

One of the most visible ways AI improves productivity is through automation of repetitive tasks. Tasks that once required hours of human effort, such as data entry, scheduling meetings, or sorting emails, can now be completed in seconds by AI-powered software. For instance, email filtering systems use machine learning algorithms to identify spam, categorize messages, and even suggest appropriate responses. This allows employees to focus their time and mental energy on more complex and creative work that requires human judgment and emotional intelligence.

Customer service has been revolutionized by AI chatbots and virtual assistants. These systems can handle thousands of customer inquiries simultaneously, providing instant responses 24 hours a day, seven days a week. Unlike human agents who need breaks and can only assist one customer at a time, AI systems maintain consistent quality and can learn from each interaction to improve their performance. Companies report that implementing chatbots has reduced customer service costs by up to 30% while actually improving customer satisfaction scores because of faster response times.

Robot trí tuệ nhân tạo hỗ trợ khách hàng trong môi trường văn phòng hiện đại tăng năng suất làm việc

In the manufacturing sector, AI-powered robots work alongside human employees, taking on dangerous or physically demanding tasks. These collaborative robots, often called “cobots,” can work continuously without fatigue, maintaining precision and consistency that would be impossible for human workers over extended periods. For example, in automotive factories, AI systems can perform welding tasks with accuracy down to fractions of a millimeter, something that requires intense concentration when done manually. This partnership between humans and machines has led to significant increases in production rates while simultaneously reducing workplace injuries.

Data analysis represents another area where AI dramatically improves productivity. Modern businesses generate massive amounts of data every day, far more than human analysts could possibly examine thoroughly. AI systems can process this big data, identify patterns, and generate actionable insights within minutes. In the retail industry, AI analyzes customer behavior, purchase history, and market trends to predict which products will be in demand, allowing companies to optimize their inventory management and reduce waste. Financial institutions use AI to detect fraudulent transactions by identifying unusual patterns that might escape human notice.

The healthcare industry has embraced AI to improve both the productivity of medical professionals and patient outcomes. AI systems can analyze medical images such as X-rays and MRI scans faster than radiologists, often with comparable accuracy. This doesn’t replace doctors but allows them to see more patients and focus on complex cases requiring human expertise. AI also assists in drug discovery, analyzing molecular structures and predicting which compounds might be effective treatments, a process that traditionally took years but can now be completed in months.

However, the integration of AI into the workplace is not without challenges. One concern is the need for employee training. Workers must learn to use new AI tools effectively, which requires time and investment from employers. Additionally, there are fears about job displacement, though most experts argue that AI will transform jobs rather than eliminate them entirely. The key is for workers to develop skills that complement AI capabilities, focusing on tasks requiring creativity, emotional intelligence, and complex decision-making.

Privacy and security issues also arise with AI implementation. Systems that collect and analyze employee data must be carefully managed to protect individual privacy rights. Companies need clear policies about what data is collected, how it’s used, and who has access to it. When implemented responsibly, AI can enhance workplace productivity while respecting employee rights and maintaining a positive work environment.

Questions 1-13

Questions 1-5: Multiple Choice

Choose the correct letter, A, B, C, or D.

1. According to the passage, AI technologies have become mainstream because:
A. Only large tech companies can afford them
B. They are now more accessible to businesses of various sizes
C. Governments have made them compulsory
D. They are cheaper than hiring employees

2. Email filtering systems using AI help employees by:
A. Writing all their emails for them
B. Deleting all incoming messages
C. Categorizing messages and suggesting responses
D. Preventing them from receiving any spam

3. The main advantage of AI chatbots over human customer service agents is:
A. They are more friendly and personable
B. They can handle multiple inquiries simultaneously 24/7
C. They cost nothing to operate
D. They understand emotions better

4. In manufacturing, “cobots” are described as:
A. Robots that replace all human workers
B. Machines that only work during night shifts
C. Collaborative robots working alongside humans
D. Traditional factory equipment

5. AI systems in retail businesses are used to:
A. Replace all human sales staff
B. Predict product demand and optimize inventory
C. Design new products
D. Decorate stores attractively

Questions 6-9: True/False/Not Given

Do the following statements agree with the information in the passage?

Write:

• TRUE if the statement agrees with the information
• FALSE if the statement contradicts the information
• NOT GIVEN if there is no information on this

6. AI-powered robots in automotive factories can perform welding with extreme precision.

7. AI systems in financial institutions can detect all fraudulent transactions without any errors.

8. AI medical image analysis has completely replaced radiologists in most hospitals.

9. Most experts believe AI will transform jobs rather than completely eliminate them.

Questions 10-13: Summary Completion

Complete the summary below using NO MORE THAN TWO WORDS from the passage for each answer.

AI has transformed workplace productivity in multiple ways. It automates (10) ____ that previously consumed hours of human time. In customer service, AI can maintain (11) ____ while handling numerous inquiries simultaneously. However, implementing AI presents challenges including the need for (12) __ and concerns about job displacement. Companies must also address (13) __ issues when collecting and analyzing employee data.

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PASSAGE 2 – The Economic Impact of AI-Driven Productivity Gains

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The proliferation of artificial intelligence across industries has sparked intense debate among economists regarding its long-term impact on economic growth and labor markets. While the productivity gains from AI are undeniable, understanding their macroeconomic implications requires examining both the immediate benefits and potential structural changes to how economies function. This paradigm shift mirrors historical technological revolutions, yet the pace and scope of AI’s influence present unprecedented challenges and opportunities.

Productivity, defined as output per unit of input, stands at the core of economic prosperity. Throughout history, major productivity increases have followed technological breakthroughs – from the steam engine to electricity to computers. AI represents the latest chapter in this narrative, but with a crucial difference: its ability to perform cognitive tasks previously thought to be exclusively human domains. Unlike mechanical automation that replaced physical labor, AI can analyze, decide, and even create, augmenting or replacing knowledge work that forms the backbone of modern economies. Early empirical evidence suggests that companies adopting AI technologies experience productivity gains ranging from 20% to 40%, depending on the sector and implementation sophistication.

The sectoral distribution of AI’s productivity impact reveals heterogeneous effects. Professional services, particularly legal research, financial analysis, and medical diagnostics, have seen dramatic efficiency improvements. Law firms using AI-powered document review can analyze contracts in hours rather than weeks, reducing costs and allowing lawyers to focus on strategic advice rather than tedious examination. Similarly, AI algorithms in financial services can process market data and execute trades in milliseconds, a task impossible for human traders. This has led to what economists call “skill-biased technological change” – a phenomenon where technology increases demand for highly skilled workers who can leverage AI tools while potentially reducing demand for routine cognitive work.

Manufacturing and logistics demonstrate AI’s capacity to optimize complex systems. Predictive maintenance powered by AI analyzes sensor data from machinery to forecast failures before they occur, reducing downtime and maintenance costs by up to 50% in some industries. In supply chain management, AI algorithms coordinate thousands of variables – from raw material availability to transportation costs to demand forecasts – creating efficiency gains that humans coordinating manually could never achieve. Amazon’s fulfillment centers, for instance, use AI to direct robots, manage inventory, and predict shipping times with remarkable accuracy, enabling the rapid delivery services consumers now expect.

However, translating individual company productivity gains into broader economic growth is not automatic. This phenomenon, known as the “productivity paradox,” has historical precedent. In the 1980s and early 1990s, despite massive investments in computer technology, overall productivity growth remained sluggish, leading economist Robert Solow to famously observe, “You can see the computer age everywhere but in the productivity statistics.” The lag occurred because realizing technology’s full potential required organizational restructuring, workforce retraining, and the development of complementary innovations – processes that took years to materialize.

Biểu đồ phân tích tăng trưởng năng suất lao động nhờ ứng dụng trí tuệ nhân tạo trong doanh nghiệp

Contemporary AI adoption may face similar diffusion challenges. While tech giants and well-capitalized firms rapidly integrate AI, smaller businesses often lack the infrastructure, expertise, or financial resources to implement these technologies effectively. This creates a “productivity gap” where leading firms race ahead while others fall behind, potentially increasing market concentration and economic inequality. Research by the McKinsey Global Institute suggests that early AI adopters could capture disproportionate value, with the top 10% of companies potentially doubling their cash flow by 2030, while bottom performers might experience a 20% decline.

The labor market implications of AI-driven productivity gains remain hotly contested. Optimists point to historical patterns where technological progress ultimately created more jobs than it destroyed, albeit different ones. They argue that by handling routine tasks, AI frees humans for higher-value work requiring creativity, emotional intelligence, and complex problem-solving – uniquely human capabilities. New job categories emerge, from AI trainers and ethics officers to human-AI interaction designers. The World Economic Forum estimates that while AI might displace 85 million jobs by 2025, it could create 97 million new roles better adapted to the new division of labor between humans, machines, and algorithms.

Pessimists, conversely, worry that this transition may not be smooth or equitable. Unlike previous technological shifts that primarily affected manual laborers who could retrain for service sector jobs, AI potentially impacts knowledge workers who have fewer alternative pathways. The pace of AI advancement may outstrip society’s ability to retrain workers, creating prolonged unemployment and social disruption. Additionally, the geographic concentration of AI benefits in major tech hubs could exacerbate regional inequalities, with rust belt communities lacking new opportunities.

Policy responses to maximize AI’s productivity benefits while minimizing social costs require careful calibration. Investment in education and continuous learning programs can help workers adapt to changing skill requirements. Social safety nets may need strengthening to support those displaced during transitions. Some economists propose novel approaches like robot taxes – levies on companies replacing workers with AI – to fund retraining programs and cushion economic disruption. Others advocate for universal basic income as a mechanism to ensure broad distribution of AI-generated prosperity.

The measurement of AI’s true productivity impact faces methodological challenges. Traditional productivity metrics may not fully capture AI’s benefits, particularly improvements in product quality, customization, and consumer surplus. How does one quantify the value of AI-powered personalized medicine or education? Furthermore, some AI applications generate negative externalities – such as algorithmic bias in hiring or lending – that impose social costs not reflected in conventional productivity statistics. Developing comprehensive metrics that account for both economic and social dimensions of AI’s impact remains an important challenge for economists and policymakers.

Questions 14-26

Questions 14-18: Yes/No/Not Given

Do the following statements agree with the views of the writer in the passage?

Write:

• YES if the statement agrees with the views of the writer
• NO if the statement contradicts the views of the writer
• NOT GIVEN if it is impossible to say what the writer thinks about this

14. AI’s impact on productivity is fundamentally different from previous technological revolutions because it can perform cognitive tasks.

15. All sectors of the economy benefit equally from AI implementation.

16. The productivity paradox of the 1980s-90s proves that AI will also fail to improve overall economic growth.

17. Small businesses adopt AI technologies as quickly as large corporations.

18. The creation of new jobs will definitely compensate for all jobs displaced by AI.

Questions 19-23: Matching Headings

Choose the correct heading for sections from the passage. There are more headings than sections, so you will not use them all.

List of Headings:
i. Historical precedents for technology adoption delays
ii. Geographic inequality in AI benefits
iii. The definition and importance of productivity
iv. AI’s revolutionary approach to cognitive work
v. Sectoral variations in AI productivity gains
vi. Government regulation of AI companies
vii. Optimistic versus pessimistic views on employment
viii. The challenge of measuring AI’s true value
ix. International competition in AI development

19. Paragraph 2

20. Paragraph 3

21. Paragraph 5

22. Paragraph 8

23. Paragraph 10

Questions 24-26: Summary Completion

Complete the summary using the list of words A-J below.

AI’s economic impact involves complex trade-offs. While individual companies report productivity gains of 20-40%, translating these into (24) ____ economic growth faces challenges similar to past technological transitions. The benefits are not evenly distributed, with early adopters potentially gaining significant advantages, which could increase (25) __. Policy responses must balance maximizing productivity benefits with addressing potential social costs through education investment and (26) __.

A. broader B. slower C. inequality D. taxation E. employment F. training G. competition H. regulation I. innovation J. automation

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PASSAGE 3 – Cognitive Augmentation and the Future of Human-AI Collaboration

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The discourse surrounding artificial intelligence and productivity has increasingly shifted from a binary paradigm of humans versus machines toward a more nuanced understanding of synergistic collaboration. Contemporary research in cognitive science, organizational behavior, and human-computer interaction suggests that the most substantial productivity gains emerge not from AI replacing human capabilities but from augmenting them – creating hybrid intelligence systems where human intuition, creativity, and ethical judgment combine with AI’s computational power, pattern recognition, and tireless consistency. This conceptual reframing has profound implications for how organizations design work, develop talent, and structure decision-making processes in an increasingly AI-integrated world.

Cognitive augmentation – the use of technology to enhance human intellectual capabilities – represents a paradigm distinctly different from automation. While automation seeks to replace human involvement in tasks, augmentation aims to amplify human performance by providing tools that extend our cognitive reach. The distinction is more than semantic; it reflects fundamentally different philosophies about the relationship between humans and technology. Historical precedents for augmentation include relatively simple tools like calculators, which didn’t replace mathematicians but allowed them to tackle more complex problems by offloading routine calculations. Modern AI extends this principle to sophisticated domains: natural language processing aids writers, diagnostic algorithms assist physicians, and predictive analytics guide strategic planners. In each case, the human remains integral to the process, making final judgments and providing contextual understanding that AI lacks.

The efficacy of human-AI collaboration depends critically on appropriate task allocation – determining which elements of complex work should be performed by humans, which by AI, and which through interactive combination. Research by MIT’s Computer Science and Artificial Intelligence Laboratory has identified four categories of collaborative tasks. First, parallel processing, where humans and AI work on different aspects simultaneously – for example, AI analyzes quantitative data while humans gather qualitative insights through interviews. Second, sequential processing, where AI prepares information that humans then interpret – such as AI flagging potentially significant medical images that radiologists examine in detail. Third, iterative refinement, involving back-and-forth exchanges where human feedback improves AI performance, which in turn enhances human decision-making. Fourth, real-time integration, where AI provides continuous support during human task execution, like autocomplete suggestions while writing or navigation assistance while driving.

Empirical studies of radiologists using AI diagnostic tools illuminate the dynamics of effective collaboration. When radiologists work entirely without AI assistance, they demonstrate high accuracy but limited throughput, potentially examining 50-60 images daily. AI systems alone can process thousands of images rapidly but generate false positives that could lead to unnecessary procedures. The optimal configuration pairs radiologists with AI in a sequential workflow: AI pre-screens images, categorizing them by likelihood of abnormality. This allows radiologists to prioritize potentially serious cases while spending less time on routine scans. Studies show this collaboration increases diagnostic accuracy by 5-10% while doubling the number of patients each radiologist can serve – a multiplicative productivity gain impossible through either humans or AI working in isolation. Crucially, radiologists maintain final diagnostic authority, applying medical knowledge, patient history, and clinical judgment that contextualizes AI findings.

The organizational architecture required to support productive human-AI collaboration differs substantially from traditional hierarchical structures. Research by organizational theorists identifies several key elements. First, psychological safety – team members must feel comfortable questioning AI recommendations rather than deferring automatically to algorithmic authority. The phenomenon of “automation bias” – over-reliance on automated systems – has contributed to failures ranging from aviation accidents to medical errors. Creating cultures where humans critically evaluate AI suggestions requires leadership explicitly valuing human judgment. Second, transparent AI systems – explainable AI (XAI) that provides reasoning for its recommendations enables humans to assess validity rather than treating outputs as “black box” pronouncements. Third, continuous learning loops where human corrections of AI errors feed back into system improvement, while AI identifies gaps in human knowledge that targeted training can address.

Cảnh làm việc kết hợp giữa con người và trí tuệ nhân tạo trong môi trường văn phòng hiện đại

The cognitive science of human-AI teaming reveals both opportunities and pitfalls. Human cognition involves two processing systems: System 1 (intuitive, fast, automatic) and System 2 (analytical, slow, deliberate). AI typically excels at codified System 2 tasks involving explicit rules and large-scale data processing. However, System 1 intuition – developed through years of experience and tacit knowledge – often enables experts to make rapid judgments in ambiguous situations where complete data is unavailable. Effective collaboration requires recognizing these complementary strengths. For instance, experienced financial traders using AI predictive models often report their “gut feelings” sometimes contradict algorithmic recommendations. Research suggests the most successful traders neither dismiss their intuition nor ignore AI, but rather investigate discrepancies to understand whether qualitative factors the AI cannot process (like geopolitical tensions or leadership changes) justify deviating from model predictions.

The temporal dimension of productivity gains from human-AI collaboration unfolds across multiple timescales. Immediate gains come from task acceleration – AI completing in seconds what humans need hours to finish. Medium-term productivity increases emerge as humans develop expertise in leveraging AI tools, learning which tasks to delegate, how to formulate effective queries, and how to validate outputs. Research on programming productivity, for example, shows that developers using AI code completion tools experience only modest gains initially but achieve 40-50% productivity improvements after several months as they learn to work synergistically with the technology. Long-term gains result from structural changes in how work is organized and skills are developed, as entire workflows are reconceived around human-AI collaboration rather than simply adding AI to existing processes.

The pedagogical implications of AI-augmented productivity have received insufficient attention. If future productivity depends on effective human-AI collaboration, educational systems must prepare students accordingly. This extends beyond teaching AI literacy – understanding what AI is and how it works – to developing metacognitive skills for orchestrating collaborative intelligence. Students need practice deciding when to rely on AI assistance versus working independently, how to critically assess algorithmic outputs, and how to provide feedback that improves AI performance. Early experiments with AI writing assistants in education demonstrate the dilemma: students using AI tools produce higher-quality work more efficiently, yet concerns arise about whether over-reliance on technological support might atrophy fundamental skills. Resolving this tension requires pedagogical frameworks that treat AI as a scaffolding tool – supporting skill development while ensuring students master underlying competencies.

The ethical dimensions of AI-augmented productivity warrant careful consideration. As AI systems become more capable, the locus of responsibility in human-AI collaborative decisions becomes ambiguous. When an AI-assisted doctor misdiagnoses a condition, or an AI-advised investment manager makes unprofitable trades, determining accountability becomes complex. Legal and professional frameworks traditionally assume individual human responsibility for decisions, but this assumption erodes when decisions emerge from opaque interactions between human judgment and algorithmic recommendations. Establishing clear accountability structures while maintaining the flexibility that makes human-AI collaboration productive represents an ongoing challenge requiring cross-disciplinary dialogue among technologists, legal scholars, ethicists, and practitioners.

Measuring the productivity of human-AI collaboration presents methodological challenges that transcend traditional metrics. Output-per-hour calculations may capture quantitative gains but miss qualitative improvements in decision quality, innovation, or risk management. Furthermore, optimal collaboration might sometimes sacrifice short-term productivity for long-term resilience – for example, taking time to understand AI reasoning rather than automatically accepting recommendations builds human expertise that prevents costly errors later. Developing holistic evaluation frameworks that account for multiple dimensions of productivity across different timescales remains an important research frontier. Some organizations are experimenting with balanced scorecards that measure not only output but also decision quality, learning velocity, and adaptability – acknowledging that sustainable productivity in dynamic environments requires more than maximizing immediate throughput.

The trajectory toward AI-augmented productivity is not deterministic but shaped by choices organizations and societies make about technology design, implementation, and governance. Technologies that treat human expertise as a constraint to be minimized will produce different outcomes than those designed explicitly to enhance human capabilities. As AI systems become increasingly sophisticated, maintaining meaningful human agency requires intentional design choices that preserve human autonomy, support skill development, and create transparency. The most promising path forward lies not in choosing between human and artificial intelligence but in cultivating their synergistic combination – recognizing that the most profound productivity gains emerge when we augment rather than replace the irreplaceable dimensions of human capability.

Questions 27-40

Questions 27-31: Multiple Choice

Choose the correct letter, A, B, C, or D.

27. According to the passage, cognitive augmentation differs from automation in that it:
A. Completely replaces human workers with machines
B. Seeks to enhance human intellectual capabilities rather than replace them
C. Is less expensive to implement in organizations
D. Only works in manufacturing environments

28. The MIT research identified four categories of collaborative tasks. Which is NOT mentioned?
A. Parallel processing where humans and AI work simultaneously
B. Sequential processing where AI prepares information for humans
C. Competitive processing where humans and AI race against each other
D. Real-time integration where AI provides continuous support

29. The radiologist example demonstrates that optimal productivity occurs when:
A. AI works completely independently
B. Radiologists ignore AI recommendations entirely
C. AI pre-screens images and radiologists provide final judgment
D. Radiologists only examine images flagged as normal

30. “Automation bias” refers to:
A. Prejudice against using automated systems
B. Over-reliance on automated systems without critical evaluation
C. The tendency for AI to make biased decisions
D. Human preference for manual work over automation

31. According to research on programming productivity, developers using AI tools:
A. Experience maximum productivity gains immediately
B. Show no improvement over time
C. Achieve 40-50% productivity improvements after several months
D. Become less productive as they rely on AI

Questions 32-36: Matching Features

Match each description (32-36) with the correct timescale of productivity gains (A, B, or C). You may use any letter more than once.

A. Immediate gains
B. Medium-term gains
C. Long-term gains

32. Result from AI completing tasks in seconds that humans need hours to finish

33. Emerge as humans develop expertise in leveraging AI tools effectively

34. Occur when entire workflows are reconceived around human-AI collaboration

35. Come from learning which tasks to delegate to AI systems

36. Result from structural changes in how work is organized

Questions 37-40: Short-answer Questions

Answer the questions below using NO MORE THAN THREE WORDS from the passage for each answer.

37. What type of AI provides reasoning for its recommendations, enabling humans to assess validity?

38. According to cognitive science, which processing system is fast, automatic, and intuitive?

39. What term describes the educational approach that treats AI as a tool supporting skill development?

40. What must be established to clarify responsibility when AI-assisted decisions lead to errors?

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Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. B
4. C
5. B
6. TRUE
7. NOT GIVEN
8. FALSE
9. TRUE
10. repetitive tasks
11. consistent quality
12. employee training
13. privacy (and security)

PASSAGE 2: Questions 14-26

14. YES
15. NO
16. NO
17. NO
18. NOT GIVEN
19. iv
20. v
21. i
22. vii
23. viii
24. A (broader)
25. C (inequality)
26. F (training)

PASSAGE 3: Questions 27-40

27. B
28. C
29. C
30. B
31. C
32. A
33. B
34. C
35. B
36. C
37. Explainable AI (XAI)
38. System 1
39. Scaffolding tool
40. Accountability structures

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Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI technologies, mainstream
• Vị trí trong bài: Đoạn 1, dòng 2-4
• Giải thích: Bài đọc nói rõ “This shift has been driven by the accessibility of AI platforms” – nghĩa là sự dễ tiếp cận của AI đã thúc đẩy việc áp dụng rộng rãi. Đáp án B paraphrase ý này thành “more accessible to businesses of various sizes” (dễ tiếp cận hơn với các doanh nghiệp mọi quy mô).

Câu 2: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Email filtering systems, help employees
• Vị trí trong bài: Đoạn 2, dòng 4-6
• Giải thích: Bài viết chỉ ra “email filtering systems use machine learning algorithms to identify spam, categorize messages, and even suggest appropriate responses” – đáp án C tóm tắt chính xác các chức năng này.

Câu 3: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI chatbots, advantage, human agents
• Vị trí trong bài: Đoạn 3, dòng 2-4
• Giải thích: “These systems can handle thousands of customer inquiries simultaneously, providing instant responses 24 hours a day” – đây chính là lợi thế chính được nhấn mạnh trong đoạn văn.

Câu 6: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: automotive factories, welding, precision
• Vị trí trong bài: Đoạn 4, dòng 5-7
• Giải thích: Bài đọc nêu rõ “AI systems can perform welding tasks with accuracy down to fractions of a millimeter” – khẳng định sự chính xác cao trong hàn xì.

Câu 8: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI medical image analysis, replaced radiologists
• Vị trí trong bài: Đoạn 6, dòng 3-5
• Giải thích: Bài viết nói “This doesn’t replace doctors but allows them to see more patients” – rõ ràng mâu thuẫn với việc AI thay thế hoàn toàn bác sĩ X-quang.

Câu 10-13: Summary Completion

• Các đáp án được lấy trực tiếp từ đoạn 2 (repetitive tasks), đoạn 3 (consistent quality), đoạn 7 (employee training), và đoạn 8 (privacy).

Passage 2 – Giải Thích

Câu 14: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: AI impact, fundamentally different, cognitive tasks
• Vị trí trong bài: Đoạn 2, dòng 4-7
• Giải thích: Tác giả khẳng định “AI represents the latest chapter… with a crucial difference: its ability to perform cognitive tasks” – đồng ý rằng AI khác biệt cơ bản so với các cuộc cách mạng công nghệ trước đây.

Câu 15: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: sectors, benefit equally
• Vị trí trong bài: Đoạn 3, dòng 1-2
• Giải thích: “The sectoral distribution of AI’s productivity impact reveals heterogeneous effects” – từ “heterogeneous” (không đồng nhất) chỉ ra rằng các ngành hưởng lợi khác nhau, mâu thuẫn với “equally” trong câu hỏi.

Câu 17: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: Small businesses, adopt AI, as quickly as
• Vị trí trong bài: Đoạn 6, dòng 2-4
• Giải thích: “While tech giants and well-capitalized firms rapidly integrate AI, smaller businesses often lack the infrastructure…” – rõ ràng các doanh nghiệp nhỏ KHÔNG áp dụng nhanh như các công ty lớn.

Câu 19-23: Matching Headings

Câu 19: iv (AI’s revolutionary approach to cognitive work)

• Đoạn 2 tập trung vào việc AI có thể thực hiện các nhiệm vụ nhận thức, điều mà các công nghệ trước không làm được.

Câu 20: v (Sectoral variations in AI productivity gains)

• Đoạn 3 nói rõ về “sectoral distribution” và “heterogeneous effects” – sự khác biệt giữa các ngành.

Câu 21: i (Historical precedents for technology adoption delays)

• Đoạn 5 đề cập đến “productivity paradox” và “historical precedent” của máy tính trong những năm 1980-90.

Câu 24-26: Summary Completion

• 24: A (broader) – “broader economic growth” phù hợp với ngữ cảnh mở rộng phạm vi
• 25: C (inequality) – văn cảnh nói về khoảng cách giữa những người áp dụng sớm và muộn
• 26: F (training) – phù hợp với việc đầu tư vào giáo dục được nhắc đến trong bài

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: cognitive augmentation, differs from automation
• Vị trí trong bài: Đoạn 2, dòng 1-3
• Giải thích: “While automation seeks to replace human involvement in tasks, augmentation aims to amplify human performance” – đáp án B tóm gọn ý này.

Câu 28: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: MIT research, four categories, NOT mentioned
• Vị trí trong bài: Đoạn 3, dòng 3-10
• Giải thích: Bài liệt kê: parallel processing, sequential processing, iterative refinement, và real-time integration. KHÔNG có “competitive processing” (xử lý cạnh tranh) – đây là đáp án đúng vì đây là điều KHÔNG được nhắc đến.

Câu 29: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: radiologist example, optimal productivity
• Vị trí trong bài: Đoạn 4, dòng 5-9
• Giải thích: “The optimal configuration pairs radiologists with AI in a sequential workflow: AI pre-screens images… allows radiologists to prioritize potentially serious cases” – chính xác là đáp án C.

Câu 30: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: automation bias
• Vị trí trong bài: Đoạn 5, dòng 5-6
• Giải thích: “The phenomenon of ‘automation bias’ – over-reliance on automated systems” – định nghĩa rõ ràng automation bias là sự quá phụ thuộc vào hệ thống tự động.

Câu 37: Explainable AI (XAI)

• Vị trí: Đoạn 5, dòng 9-10
• Giải thích: “transparent AI systems – explainable AI (XAI) that provides reasoning for its recommendations”

Câu 38: System 1

• Vị trí: Đoạn 6, dòng 2-3
• Giải thích: “System 1 (intuitive, fast, automatic)” – khớp chính xác với mô tả trong câu hỏi

Câu 39: Scaffolding tool

• Vị trí: Đoạn 8, dòng 9-10
• Giải thích: “Resolving this tension requires pedagogical frameworks that treat AI as a scaffolding tool”

Câu 40: Accountability structures

• Vị trí: Đoạn 9, dòng 7-9
• Giải thích: “Establishing clear accountability structures while maintaining the flexibility…”

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Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
artificial intelligence	n	/ˌɑːtɪˈfɪʃəl ɪnˈtelɪdʒəns/	trí tuệ nhân tạo	The introduction of artificial intelligence into the workplace	artificial intelligence systems, develop artificial intelligence
automation	n	/ˌɔːtəˈmeɪʃən/	tự động hóa	One of the most visible ways AI improves productivity is through automation	factory automation, automation technology
productivity	n	/ˌprɒdʌkˈtɪvəti/	năng suất	AI technologies offer clear benefits in terms of productivity enhancement	increase productivity, productivity gains
chatbot	n	/ˈtʃætbɒt/	robot trò chuyện tự động	Customer service has been revolutionized by AI chatbots	AI chatbot, customer service chatbot
collaborative robots	n	/kəˈlæbərətɪv ˈrəʊbɒts/	robot cộng tác	These collaborative robots, often called “cobots”	collaborative robots work, deploy collaborative robots
precision	n	/prɪˈsɪʒən/	độ chính xác	AI systems can perform welding tasks with precision	high precision, precision manufacturing
data analysis	n	/ˈdeɪtə əˈnæləsɪs/	phân tích dữ liệu	Data analysis represents another area where AI improves productivity	conduct data analysis, advanced data analysis
pattern	n	/ˈpætən/	khuôn mẫu, mô hình	AI systems can identify patterns and generate insights	identify patterns, recognize patterns
inventory management	n	/ˈɪnvəntri ˈmænɪdʒmənt/	quản lý hàng tồn kho	AI allows companies to optimize their inventory management	efficient inventory management, inventory management system
fraudulent	adj	/ˈfrɔːdʒələnt/	gian lận, lừa đảo	Financial institutions use AI to detect fraudulent transactions	fraudulent activity, fraudulent transactions
displacement	n	/dɪsˈpleɪsmənt/	sự thay thế, mất việc	There are fears about job displacement	job displacement, worker displacement
complement	v	/ˈkɒmplɪment/	bổ sung	Workers must develop skills that complement AI capabilities	complement each other, complement skills

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
proliferation	n	/prəˌlɪfəˈreɪʃən/	sự gia tăng nhanh	The proliferation of artificial intelligence across industries	nuclear proliferation, proliferation of technology
macroeconomic	adj	/ˌmækrəʊˌiːkəˈnɒmɪk/	thuộc kinh tế vĩ mô	Understanding their macroeconomic implications	macroeconomic policy, macroeconomic indicators
unprecedented	adj	/ʌnˈpresɪdentɪd/	chưa từng có	The pace and scope present unprecedented challenges	unprecedented growth, unprecedented crisis
augmenting	v	/ɔːɡˈmentɪŋ/	tăng cường, bổ sung	AI can analyze, decide, and even create, augmenting knowledge work	augmenting capabilities, augmenting workforce
heterogeneous	adj	/ˌhetərəˈdʒiːniəs/	không đồng nhất	The sectoral distribution reveals heterogeneous effects	heterogeneous group, heterogeneous mixture
algorithm	n	/ˈælɡərɪðəm/	thuật toán	AI algorithms in financial services can process market data	complex algorithm, machine learning algorithm
predictive maintenance	n	/prɪˈdɪktɪv ˈmeɪntənəns/	bảo trì dự đoán	Predictive maintenance powered by AI analyzes sensor data	implement predictive maintenance, predictive maintenance system
supply chain	n	/səˈplaɪ tʃeɪn/	chuỗi cung ứng	In supply chain management, AI algorithms coordinate thousands of variables	global supply chain, supply chain optimization
productivity paradox	n	/ˌprɒdʌkˈtɪvəti ˈpærədɒks/	nghịch lý năng suất	This phenomenon, known as the “productivity paradox”	explain the productivity paradox
diffusion	n	/dɪˈfjuːʒən/	sự lan tràn, phổ biến	Contemporary AI adoption may face similar diffusion challenges	technology diffusion, diffusion of innovation
market concentration	n	/ˈmɑːkɪt ˌkɒnsənˈtreɪʃən/	sự tập trung thị trường	This creates a productivity gap, potentially increasing market concentration	high market concentration, market concentration ratio
equity	n	/ˈekwəti/	công bằng	This transition may not be equitable	social equity, equity in education
calibration	n	/ˌkælɪˈbreɪʃən/	sự điều chỉnh cẩn thận	Policy responses require careful calibration	careful calibration, precise calibration
robot tax	n	/ˈrəʊbɒt tæks/	thuế robot	Some economists propose robot taxes	implement robot tax, robot tax policy
externality	n	/ˌekstɜːˈnæləti/	yếu tố bên ngoài	Some AI applications generate negative externalities	negative externality, positive externality

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
synergistic	adj	/ˌsɪnəˈdʒɪstɪk/	hiệp đồng, cộng hưởng	Creating synergistic collaboration between humans and AI	synergistic effect, synergistic relationship
cognitive augmentation	n	/ˈkɒɡnətɪv ˌɔːɡmenˈteɪʃən/	tăng cường nhận thức	Cognitive augmentation enhances human intellectual capabilities	cognitive augmentation technology
paradigm	n	/ˈpærədaɪm/	mô hình tư duy	This conceptual reframing has profound implications for the paradigm	paradigm shift, new paradigm
offloading	v	/ˈɒfləʊdɪŋ/	giảm tải	Calculators allowed mathematicians to tackle problems by offloading routine calculations	offloading tasks, offloading responsibility
diagnostic algorithm	n	/ˌdaɪəɡˈnɒstɪk ˈælɡərɪðəm/	thuật toán chẩn đoán	Diagnostic algorithms assist physicians	develop diagnostic algorithm
task allocation	n	/tɑːsk ˌæləˈkeɪʃən/	phân bổ nhiệm vụ	The efficacy depends on appropriate task allocation	efficient task allocation, task allocation strategy
false positive	n	/fɔːls ˈpɒzətɪv/	kết quả dương tính giả	AI systems alone can generate false positives	reduce false positives, false positive rate
multiplicative	adj	/ˌmʌltɪˈplɪkətɪv/	nhân lên	This collaboration creates multiplicative productivity gains	multiplicative effect, multiplicative factor
automation bias	n	/ˌɔːtəˈmeɪʃən ˈbaɪəs/	thiên lệch tự động hóa	The phenomenon of automation bias has contributed to failures	overcome automation bias, automation bias effect
explainable AI	n	/ɪkˈspleɪnəbl eɪ aɪ/	AI có thể giải thích	Explainable AI provides reasoning for its recommendations	develop explainable AI, explainable AI system
tacit knowledge	n	/ˈtæsɪt ˈnɒlɪdʒ/	kiến thức ngầm	System 1 intuition developed through tacit knowledge	transfer tacit knowledge, tacit knowledge sharing
geopolitical	adj	/ˌdʒiːəʊpəˈlɪtɪkəl/	thuộc địa chính trị	Qualitative factors like geopolitical tensions	geopolitical risk, geopolitical situation
metacognitive	adj	/ˌmetəˈkɒɡnətɪv/	siêu nhận thức	Developing metacognitive skills for orchestrating intelligence	metacognitive strategies, metacognitive awareness
scaffolding	n	/ˈskæfəldɪŋ/	giàn giáo, công cụ hỗ trợ	Pedagogical frameworks that treat AI as a scaffolding tool	scaffolding approach, learning scaffolding
accountability	n	/əˌkaʊntəˈbɪləti/	trách nhiệm giải trình	Determining accountability becomes complex	corporate accountability, accountability structure
deterministic	adj	/dɪˌtɜːmɪˈnɪstɪk/	định trước, tất định	The trajectory toward AI-augmented productivity is not deterministic	deterministic model, deterministic approach
cultivating	v	/ˈkʌltɪveɪtɪŋ/	nuôi dưỡng, phát triển	The most promising path lies in cultivating their synergistic combination	cultivating relationships, cultivating skills

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Kết bài

Chủ đề “The role of AI in improving productivity” không chỉ phổ biến trong IELTS Reading mà còn phản ánh một xu hướng toàn cầu đang định hình lại cách chúng ta làm việc. Qua ba passages với độ khó tăng dần, bạn đã được làm quen với nhiều góc độ khác nhau về AI và năng suất – từ ứng dụng cơ bản trong văn phòng, tác động kinh tế vĩ mô, cho đến sự cộng tác sâu sắc giữa trí tuệ con người và máy móc.

Đề thi mẫu này cung cấp đầy đủ 40 câu hỏi với 7 dạng bài khác nhau, giúp bạn trải nghiệm gần như hoàn toàn với bài thi IELTS Reading thực tế. Các đáp án chi tiết kèm giải thích không chỉ cho bạn biết câu trả lời đúng mà còn hướng dẫn cách tìm thông tin, nhận diện paraphrase, và áp dụng các kỹ thuật làm bài hiệu quả. Để hiểu rõ hơn về Impacts of AI on traditional employment, bạn có thể tham khảo thêm các bài viết chuyên sâu về chủ đề này.

Hơn 40 từ vựng được tổng hợp trong bảng từ vựng không chỉ hữu ích cho IELTS Reading mà còn giúp bạn nâng cao vốn từ học thuật cho các phần thi khác như Writing và Speaking. Hãy dành thời gian học kỹ những từ này, đặc biệt chú ý đến collocations để sử dụng tự nhiên hơn. Việc áp dụng AI trong các lĩnh vực khác cũng rất đáng quan tâm, chẳng hạn như AI for reducing water waste trong quản lý tài nguyên môi trường.

Hãy nhớ rằng thành công trong IELTS Reading không chỉ đến từ việc hiểu tiếng Anh mà còn từ khả năng quản lý thời gian, xác định thông tin nhanh chóng và áp dụng chiến lược phù hợp với từng dạng câu hỏi. Luyện tập thường xuyên với các đề thi chất lượng như thế này sẽ giúp bạn xây dựng sự tự tin và đạt band điểm mong muốn. Bên cạnh đó, việc tìm hiểu về How to support mental health for working parents cũng cho thấy mối liên hệ giữa công nghệ và các vấn đề xã hội hiện đại.

Chúc bạn ôn tập hiệu quả và thành công rực rỡ trong kỳ thi IELTS sắp tới! Các chủ đề về công nghệ và tác động xã hội thường xuất hiện song hành, giống như mối quan hệ giữa Renewable energy’s impact on rural communities và sự phát triển bền vững, hay Economic migration impact on host countries trong bối cảnh toàn cầu hóa.